Exploiting Multi-Temporal Information for SAR Image Segmentation

Earth observation is taking more and more importance in our society. Indeed, recent technologies march allows for satellite images to get ground resolution up to less than one meter. Thus, most of satellite image information obtained begins now to be widely used for multiple purposes ranging from cartography and mapping to agricultural management and security applications. Among all the satellite imaging systems, Synthetic Aperture Radar (or SAR) are very interesting for global earth surveillance and monitoring purposes as they can provide data at any time under all weather conditions. However only few work have been done to take advantage of such time robustness properties for retrieving the images main structures. In this study we focused one the particular issue of obtaining an efficient closed region segmentation using multi-temporal SAR image series. To this end, several tools have been developed. In order to remove SAR inherent noise we filtered the image sequences using a multi-temporal anisotropic non-linear diffusion algorithm. Then a new approach of Canny edge detection have been introduced to extract edge features from these vector-valued images. Finally, a region segmentation step has been applied to get closed regions. We demonstrated that for all the methods, exploiting multi-temporal information can improve the results accuracy. Moreover, since some radiometric changes can occur within region due to man intervention or natural diseases we also presented a direct application of region segmentation to retrieve such changes across time series.